Abstract
Melanoma is a form of cancer that begins in melanocytes. The occurrence of melanoma continues to rise across the world and current therapeutic options are of limited benefit. Researchers are studying the genetic changes in skin tissue linked to a life-threatening melanoma through SNP genotyping, Expression microarrays, RNA interference etc. In the spectrum of disease, identification and characterization of melanoma proteins is also very important task. In the present study, effort has been made to identify the melanoma protein through Support Vector Machine. A positive dataset has been prepared through databases and literature whereas negative dataset consist of core metabolic proteins. Total 420 compositional properties of amino acid dipeptide and multiplet frequencies have been used to develop SVM model classifier. Average performance of models varies from 0.65-0.80 Mathew’s correlation coefficient values and 91.56% accuracy has been achieved through random data set.
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Rathore, B., Kushwaha, S.K., Shakya, M. (2010). Identification of Melanoma (Skin Cancer) Proteins through Support Vector Machine. In: Das, V.V., Vijaykumar, R. (eds) Information and Communication Technologies. ICT 2010. Communications in Computer and Information Science, vol 101. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15766-0_97
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DOI: https://doi.org/10.1007/978-3-642-15766-0_97
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